RIPPLE: Concept-Based Interpretation for Raw Time Series Models in Education
Project Overview
The document explores the innovative use of generative AI in education through the Ripple methodology, which leverages raw time series data from Massive Open Online Courses (MOOCs) to predict student performance early in their learning journey. By integrating advanced techniques such as graph neural networks and concept activation vectors, Ripple not only matches or surpasses the accuracy of traditional models that rely on hand-crafted features but also improves interpretability for educators. This capability allows for tailored interventions that address individual student needs, thereby enhancing the overall educational experience. The findings underscore the potential of generative AI to transform educational assessment and support, facilitating proactive measures that promote student success and engagement in online learning environments.
Key Applications
Ripple methodology for early student success prediction
Context: Massive Open Online Courses (MOOCs), targeting educators and learners
Implementation: Using raw multivariate time series data and graph neural networks without feature extraction
Outcomes: Achieves comparable or better accuracy for predicting student pass-fail labels, provides interpretable insights on student behaviors
Challenges: Requires careful handling of irregularly sampled data, ensuring effective interpretability for educators
Implementation Barriers
Technical Barrier
The challenge of processing irregular and multivariate time series data effectively.
Proposed Solutions: Utilizing graph neural networks to model dependencies in the data.
Interpretability Barrier
Deep learning models often lack transparency, making it difficult for educators to trust their predictions.
Proposed Solutions: Implementing concept activation vectors (TCA V) to provide human-friendly interpretations of model predictions.
Project Team
Mohammad Asadi
Researcher
Vinitra Swamy
Researcher
Jibril Frej
Researcher
Julien Vignoud
Researcher
Mirko Marras
Researcher
Tanja Käser
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Mohammad Asadi, Vinitra Swamy, Jibril Frej, Julien Vignoud, Mirko Marras, Tanja Käser
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai